The invention relates generally to graphical modeling of a network and in particular to graphical modeling of a network based on symbol sequences.
A wide variety of techniques are employed to visualize and/or analyze various network relationship, such as blood vessel networks, neural networks, electrical circuit networks, power system networks, communication networks transport networks or any other network. One of the popular techniques of representing a network relationship is by providing a graphical representation of the network. Though the graphical representation of the network relationship enables visualization of the network relationship and associated problems, the analysis is typically performed manually.
For example, in a medical imaging application, a blood vessel network is manually analyzed to identify potentially life-threatening aneurysms, or to plan surgical operations, or for other purposes. Alternatively, computer implemented techniques may be employed for the analysis of the graphical representation of the network. However, these techniques generally employ a brute force graph search algorithm that may be computationally very intensive even for small graphs. In medical imaging applications, atlases are useful frameworks for the representation of anatomy derived from imaging data such as CT scan or MRI scan. Such atlases are used for finding and comparing “normal” and “abnormal” regions. However, a limitation of such a method requires spatial correspondence between the atlas and the case being compared with. Thus, a spatial registration is performed between the atlas and the case under comparison that is complex and prone to errors. An equivalent to an atlas in non-medical applications would include a standardized circuit diagram. The current techniques are therefore cumbersome and inefficient.
It is therefore desirable to provide an efficient method for intelligent, automatic, and interactive visualization and analysis of various network relationships. It is also desirable to provide a graphical representation of a network relationship that is spatially independent.